{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. 导入需要的库\n",
    "import matplotlib.pyplot as plt  # 核心绘图库（所有图表功能都依赖它）\n",
    "import pandas as pd  # 数据处理库（用于生成日期数据）\n",
    "import numpy as np  # 数值计算库（此处未直接用，但后续扩展可能用到）\n",
    "from matplotlib.dates import DateFormatter, MonthLocator  # 导入定位器\n",
    "\n",
    "\n",
    "# 2. 解决中文乱码（关键：避免标题/标签中文显示方块）\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows指定黑体（Mac/Linux见之前说明）\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示为方块的问题\n",
    "\n",
    "# 3. 创建示例数据\n",
    "# 生成12个日期（2023-01-01开始，每月月末，共12个点）\n",
    "dates = pd.date_range('2023-01-01', periods=12, freq='ME')\n",
    "sales_a = [120, 135, 148, 165, 180, 195, 210, 225, 240, 255, 270, 285]  # 产品A销售额\n",
    "sales_b = [100, 115, 130, 145, 160, 170, 175, 185, 195, 205, 215, 225]  # 产品B销售额\n",
    "\n",
    "# 4. 【核心步骤1：创建图表画布+子图对象】\n",
    "# fig：整个图表的“画布”（容器，包含所有元素）\n",
    "# ax：子图对象（实际绘图的区域，一个画布可包含多个子图）\n",
    "# figsize=(12,6)：设置画布大小（宽12英寸，高6英寸，数值越大图越大）\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# 5. 【核心步骤2：声明图表类型+绘图】\n",
    "# --------------- 绘制产品A的折线图 ---------------\n",
    "# ax.plot()：明确声明“绘制折线图”（matplotlib中plot默认就是折线图）\n",
    "# 参数1：x轴数据（dates：日期）\n",
    "# 参数2：y轴数据（sales_a：产品A销售额）\n",
    "# marker='o'：数据点标记为“圆形”（o=圆形，s=方形，^=三角形等）\n",
    "# linewidth=2：线条宽度（数值越大线越粗）\n",
    "# label='产品A'：图例名称（后续legend()会显示）\n",
    "# color='#2E86AB'：线条颜色（十六进制色号，也可写'red'/'blue'等）\n",
    "ax.plot(dates, sales_a, marker='o', linewidth=2, label='产品A', color='#2E86AB')\n",
    "\n",
    "# --------------- 绘制产品B的折线图 ---------------\n",
    "# 逻辑和产品A一致，仅修改数据、标记形状、颜色\n",
    "ax.plot(dates, sales_b, marker='s', linewidth=2, label='产品B', color='#A23B72')\n",
    "\n",
    "# 6. 图表装饰（优化显示效果，非核心但必要）\n",
    "# 设置图表标题（fontsize=字体大小，fontweight='bold'=加粗，pad=标题与图表的间距）\n",
    "ax.set_title('月度销售趋势分析', fontsize=16, fontweight='bold', pad=20)\n",
    "# 设置x轴标签（说明x轴数据含义）\n",
    "ax.set_xlabel('月份', fontsize=12)\n",
    "# 设置y轴标签（说明y轴数据含义）\n",
    "ax.set_ylabel('销售额（万元）', fontsize=12)\n",
    "# 设置图例（显示label对应的名称）\n",
    "# loc='upper left'：图例位置（左上角）\n",
    "# frameon=True：显示图例边框，fancybox=True：边框圆角，shadow=True：阴影效果\n",
    "ax.legend(loc='upper left', frameon=True, fancybox=True, shadow=True)\n",
    "# 显示网格线（alpha=0.3：透明度，0=完全透明，1=不透明）\n",
    "ax.grid(True, alpha=0.5)\n",
    "\n",
    "# 7. 优化x轴日期显示（避免日期重叠）\n",
    "# 设置x轴刻度格式：将日期显示为“年-月”（如2023-01）\n",
    "ax.xaxis.set_major_locator(MonthLocator())  # 强制每月显示1个刻度\n",
    "ax.xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%Y-%m'))\n",
    "# 旋转x轴刻度标签45度（避免文字重叠）\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# 8. 自动调整布局（避免标签/图例被截断）\n",
    "plt.tight_layout()\n",
    "\n",
    "# 9. 显示图表（执行后弹出窗口展示图表）\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 解决中文乱码\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 1. 数据准备（日期格式适配柱状图：提取“年-月”字符串，避免x轴混乱）\n",
    "dates = pd.date_range('2023-01-01', periods=12, freq='ME')\n",
    "x_labels = [d.strftime('%Y-%m') for d in dates]  # 转换为“2023-01”格式的标签\n",
    "sales_a = [120, 135, 148, 165, 180, 195, 210, 225, 240, 255, 270, 285]\n",
    "sales_b = [100, 115, 130, 145, 160, 170, 175, 185, 195, 205, 215, 225]\n",
    "\n",
    "# 2. 创建画布和子图\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# 3. 【核心修改：用 ax.bar() 声明柱状图】\n",
    "bar_width = 0.35  # 柱子宽度（0-1之间，越小越窄，避免重叠）\n",
    "x = np.arange(len(x_labels))  # x轴位置（0,1,2,...,11，对应12个月份）\n",
    "\n",
    "# 产品A的柱子：位置在 x - bar_width/2（左移半宽，避免和B重叠）\n",
    "ax.bar(x - bar_width/2, sales_a, width=bar_width, label='产品A', color='#2E86AB')\n",
    "# 产品B的柱子：位置在 x + bar_width/2（右移半宽）\n",
    "ax.bar(x + bar_width/2, sales_b, width=bar_width, label='产品B', color='#A23B72')\n",
    "\n",
    "# 4. 图表装饰（适配柱状图调整x轴标签）\n",
    "ax.set_title('月度销售对比柱状图', fontsize=16, fontweight='bold', pad=20)\n",
    "ax.set_xlabel('月份', fontsize=12)\n",
    "ax.set_ylabel('销售额（万元）', fontsize=12)\n",
    "ax.legend(loc='upper left', frameon=True, fancybox=True, shadow=True)\n",
    "ax.grid(True, alpha=0.3, axis='y')  # 只显示y轴网格线（柱状图更清晰）\n",
    "\n",
    "# 关键：将x轴位置（0-11）替换为“年-月”标签\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(x_labels, rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        (7, 8]\n",
       "1        (0, 5]\n",
       "2        (6, 7]\n",
       "3        (8, 9]\n",
       "4       (9, 10]\n",
       "         ...   \n",
       "395      (0, 5]\n",
       "396      (5, 6]\n",
       "397      (5, 6]\n",
       "398    (10, 11]\n",
       "399      (5, 6]\n",
       "Name: sleep_duration, Length: 400, dtype: category\n",
       "Categories (8, interval[int64, right]): [(0, 5] < (5, 6] < (6, 7] < (7, 8] < (8, 9] < (9, 10] < (10, 11] < (11, 12]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/sleep.csv\")\n",
    "\n",
    "sleep_duration_stage = pd.cut(df[\"sleep_duration\"], [0, 5, 6, 7, 8, 9, 10, 11, 12])# 对睡眠时间进行划分\n",
    "sleep_duration_stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       (5.5, 7.75]\n",
       "1       (3.25, 5.5]\n",
       "2       (3.25, 5.5]\n",
       "3      (7.75, 10.0]\n",
       "4       (3.25, 5.5]\n",
       "           ...     \n",
       "395     (5.5, 7.75]\n",
       "396    (7.75, 10.0]\n",
       "397    (7.75, 10.0]\n",
       "398    (7.75, 10.0]\n",
       "399     (5.5, 7.75]\n",
       "Name: stress_level, Length: 400, dtype: category\n",
       "Categories (4, interval[float64, right]): [(0.991, 3.25] < (3.25, 5.5] < (5.5, 7.75] < (7.75, 10.0]]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stress_level_stage = pd.cut(df[\"stress_level\"], 4)\n",
    "stress_level_stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              sleep_quality\n",
      "sleep_duration stress_level                \n",
      "(0, 5]         (0.991, 3.25]       6.781818\n",
      "               (3.25, 5.5]         6.161538\n",
      "               (5.5, 7.75]         5.677778\n",
      "               (7.75, 10.0]        6.082353\n",
      "(5, 6]         (0.991, 3.25]       5.876923\n",
      "               (3.25, 5.5]         6.777778\n",
      "               (5.5, 7.75]         6.058333\n",
      "               (7.75, 10.0]        6.438462\n",
      "(6, 7]         (0.991, 3.25]       6.472727\n",
      "               (3.25, 5.5]         5.841667\n",
      "               (5.5, 7.75]         5.377778\n",
      "               (7.75, 10.0]        5.545455\n",
      "(7, 8]         (0.991, 3.25]       6.314286\n",
      "               (3.25, 5.5]         5.375000\n",
      "               (5.5, 7.75]         7.280000\n",
      "               (7.75, 10.0]        5.700000\n",
      "(8, 9]         (0.991, 3.25]       5.423077\n",
      "               (3.25, 5.5]         6.640000\n",
      "               (5.5, 7.75]         5.877778\n",
      "               (7.75, 10.0]        6.320000\n",
      "(9, 10]        (0.991, 3.25]       5.788235\n",
      "               (3.25, 5.5]         6.790000\n",
      "               (5.5, 7.75]         6.010000\n",
      "               (7.75, 10.0]        6.287500\n",
      "(10, 11]       (0.991, 3.25]       5.853333\n",
      "               (3.25, 5.5]         6.057143\n",
      "               (5.5, 7.75]         6.000000\n",
      "               (7.75, 10.0]        6.083333\n",
      "(11, 12]       (0.991, 3.25]       6.335294\n",
      "               (3.25, 5.5]         6.700000\n",
      "               (5.5, 7.75]         6.071429\n",
      "               (7.75, 10.0]        6.370588\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_3252\\1475496676.py:1: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
      "  print(df.pivot_table(values=\"sleep_quality\", index=[sleep_duration_stage, stress_level_stage], aggfunc=\"mean\"))\n"
     ]
    }
   ],
   "source": [
    "print(df.pivot_table(values=\"sleep_quality\", index=[sleep_duration_stage, stress_level_stage], aggfunc=\"mean\"))"
   ]
  }
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